Understanding the relationship between workers, worker instances, and executors is crucial for grasping how Apache Spark achieves distributed computation. Let’s break down each of these components and explain their interaction in a Spark cluster.
Workers, Worker Instances, and Executors in Apache Spark
Workers
Workers are nodes in the cluster that are responsible for executing tasks. Each worker node accepts tasks from the driver and runs them in parallel. They are part of the underlying cluster infrastructure, such as YARN, Mesos, or a standalone Spark cluster.
Worker Instances
Worker instances refer to the actual instances or physical machines/virtual machines on which the worker processes are running. In other words, a worker instance is the physical or virtual machine that hosts one or more executors. Typically, there is a one-to-one relationship between worker instances and worker processes.
Executors
Executors are processes launched on worker nodes that run tasks and store data needed for computations. Each Spark application has its own set of executors. Executors have two main roles:
- Executing code assigned by the driver.
- Storing data, either in-memory or on disk.
Executors are launched at the beginning of a Spark application and run for the application’s duration. They provide in-memory storage for RDDs that are cached by user programs and are responsible for executing tasks and returning result data to the driver.
Relationship and Interaction
To put it all together:
- The driver program (the main program) runs on the client machine and connects to the Spark cluster, typically managed by a cluster manager like YARN or Mesos.
- The cluster manager allocates resources (worker nodes) for the application.
- These worker nodes (worker instances) run the worker processes.
- Each worker process can run several executors, depending on the allocated resources and configuration settings (like the number of cores and memory).
- The executors execute the tasks sent by the driver and store data in memory or disk as required for computation.
Here’s a high-level diagram for better visualization:
+-----------------+
| Driver |
+-----------------+
|
+-----------------+
| Cluster Manager |
+-----------------+
|
Worker Instances/Nodes
+--------------+
| Worker |
| Node 1 |-------> Executor 1, Executor 2, ...
+--------------+
|
+--------------+
| Worker |
| Node 2 |-------> Executor 3, Executor 4, ...
+--------------+
With this knowledge, you now understand how workers, worker instances, and executors relate to each other and work together to run Spark applications efficiently in a distributed environment.